Swarm intelligence-based algorithm for dropped packets modeling

An enhanced predication approach for the network dropped packets problem is introduced. This work along with test results shows the possibility of guessing when the dropped packet occurs and also the source address for it. Since artificial neural networks (ANNs) algorithms are able to model nonlinear relations between different data sets, a proposed ANN based on particle swarm optimization training algorithm (PSO) is proposed. This global optimization algorithm is applied to the proposed ANN to avoid the local minima problem in the gradient descent-training algorithm and to achieve acceptable solution. The Particle swarm optimization technique is used in this work to optimize the performance of radial basis function artificial neural network (RBF-ANN). The data used in training and testing is the data collected by a particular network simulator (NS-2) program which is utilized to simulate the data for the neural network. This RBF-ANN model has been verified by comparing ANN simulated and test data. The presented results are obtained through the use of MATLAB 8.5 software from Math works.